---
title: ChatSambaNovaCloud
---

This will help you get started with SambaNovaCloud [chat models](/oss/langchain/models/). For detailed documentation of all ChatSambaNovaCloud features and configurations head to the [API reference](https://docs.sambanova.ai/cloud/docs/get-started/overview).

**[SambaNova](https://sambanova.ai/)'s** [SambaNova Cloud](https://cloud.sambanova.ai/) is a platform for performing inference with open-source models

## Overview

### Integration details

| Class | Package | Local | Serializable | JS support | Downloads | Version |
| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |
| [ChatSambaNovaCloud](https://docs.sambanova.ai/cloud/docs/get-started/overview) | [langchain-sambanova](https://python.langchain.com/docs/integrations/providers/sambanova/) | ❌ | ❌ | ❌ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain_sambanova?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain_sambanova?style=flat-square&label=%20) |

### Model features

| [Tool calling](/oss/langchain/tools) | [Structured output](/oss/langchain/structured-output) | JSON mode | [Image input](//docs/how_to/multimodal_inputs/) | Audio input | Video input | [Token-level streaming](/oss/langchain/streaming/) | Native async | [Token usage](/oss/how-to/chat_token_usage_tracking/) | [Logprobs](/oss/how-to/logprobs/) |
| :---: | :---: | :---: | :---: |  :---: | :---: | :---: | :---: | :---: | :---: |
| ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ |

## Setup

To access ChatSambaNovaCloud models you will need to create a [SambaNovaCloud](https://cloud.sambanova.ai/) account, get an API key, install the `langchain_sambanova` integration package.

```bash
pip install langchain-sambanova
```

### Credentials

Get an API Key from [cloud.sambanova.ai](https://cloud.sambanova.ai/apis) and add it to your environment variables:

``` bash
export SAMBANOVA_API_KEY="your-api-key-here"
```

```python
import getpass
import os

if not os.getenv("SAMBANOVA_API_KEY"):
    os.environ["SAMBANOVA_API_KEY"] = getpass.getpass(
        "Enter your SambaNova Cloud API key: "
    )
```

If you want to get automated tracing of your model calls you can also set your [LangSmith](https://docs.smith.langchain.com/) API key by uncommenting below:

```python
# os.environ["LANGSMITH_TRACING"] = "true"
# os.environ["LANGSMITH_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ")
```

### Installation

The LangChain **SambaNovaCloud** integration lives in the `langchain_sambanova` package:

```python
%pip install -qU langchain-sambanova
```

## Instantiation

Now we can instantiate our model object and generate chat completions:

```python
from langchain_sambanova import ChatSambaNovaCloud

llm = ChatSambaNovaCloud(
    model="Meta-Llama-3.3-70B-Instruct",
    max_tokens=1024,
    temperature=0.7,
    top_p=0.01,
)
```

## Invocation

```python
messages = [
    (
        "system",
        "You are a helpful assistant that translates English to French. "
        "Translate the user sentence.",
    ),
    ("human", "I love programming."),
]
ai_msg = llm.invoke(messages)
ai_msg
```

```output
AIMessage(content="J'adore la programmation.", additional_kwargs={}, response_metadata={'finish_reason': 'stop', 'usage': {'acceptance_rate': 7, 'completion_tokens': 8, 'completion_tokens_after_first_per_sec': 195.0204119588971, 'completion_tokens_after_first_per_sec_first_ten': 618.3422770734173, 'completion_tokens_per_sec': 53.25837044790076, 'end_time': 1731535338.1864908, 'is_last_response': True, 'prompt_tokens': 55, 'start_time': 1731535338.0133238, 'time_to_first_token': 0.13727331161499023, 'total_latency': 0.15021112986973353, 'total_tokens': 63, 'total_tokens_per_sec': 419.4096672772185}, 'model_name': 'Meta-Llama-3.1-70B-Instruct', 'system_fingerprint': 'fastcoe', 'created': 1731535338}, id='f04b7c2c-bc46-47e0-9c6b-19a002e8f390')
```

```python
print(ai_msg.content)
```

```output
J'adore la programmation.
```

## Chaining

We can [chain](/oss/how-to/sequence/) our model with a prompt template like so:

```python
from langchain_core.prompts import ChatPromptTemplate

prompt = ChatPromptTemplate(
    [
        (
            "system",
            "You are a helpful assistant that translates {input_language} "
            "to {output_language}.",
        ),
        ("human", "{input}"),
    ]
)

chain = prompt | llm
chain.invoke(
    {
        "input_language": "English",
        "output_language": "German",
        "input": "I love programming.",
    }
)
```

```output
AIMessage(content='Ich liebe das Programmieren.', additional_kwargs={}, response_metadata={'finish_reason': 'stop', 'usage': {'acceptance_rate': 2.3333333333333335, 'completion_tokens': 6, 'completion_tokens_after_first_per_sec': 106.06729752831038, 'completion_tokens_after_first_per_sec_first_ten': 204.92722183833433, 'completion_tokens_per_sec': 26.32497272023831, 'end_time': 1731535339.9997504, 'is_last_response': True, 'prompt_tokens': 50, 'start_time': 1731535339.7539687, 'time_to_first_token': 0.19864177703857422, 'total_latency': 0.22792046410696848, 'total_tokens': 56, 'total_tokens_per_sec': 245.6997453888909}, 'model_name': 'Meta-Llama-3.1-70B-Instruct', 'system_fingerprint': 'fastcoe', 'created': 1731535339}, id='dfe0bee6-b297-472e-ac9d-29906d162dcb')
```

## Streaming

```python
system = "You are a helpful assistant with pirate accent."
human = "I want to learn more about this animal: {animal}"
prompt = ChatPromptTemplate.from_messages([("system", system), ("human", human)])

chain = prompt | llm

for chunk in chain.stream({"animal": "owl"}):
    print(chunk.content, end="", flush=True)
```

```output
Yer lookin' fer some knowledge about owls, eh? Alright then, matey, settle yerself down with a pint o' grog and listen close.

Owls be a fascinatin' lot, with their big round eyes and silent wings. They be birds o' prey, which means they hunt other creatures fer food. There be over 220 species o' owls, rangin' in size from the tiny Elf Owl (which be smaller than a parrot) to the Great Grey Owl (which be as big as a small eagle).

One o' the most interestin' things about owls be their eyes. They be huge, with some species havin' eyes that be as big as their brains! This lets 'em see in the dark, which be perfect fer nocturnal huntin'. They also have special feathers on their faces that help 'em hear better, and their ears be specially designed to pinpoint sounds.

Owls be known fer their silent flight, which be due to the special shape o' their wings. They be able to fly without makin' a sound, which be perfect fer sneakin' up on prey. They also be very agile, with some species able to fly through tight spaces and make sharp turns.

Some o' the most common species o' owls include:

* Barn Owl: A medium-sized owl with a heart-shaped face and a screechin' call.
* Tawny Owl: A large owl with a distinctive hootin' call and a reddish-brown plumage.
* Great Horned Owl: A big owl with ear tufts and a deep hootin' call.
* Snowy Owl: A white owl with a round face and a soft, hootin' call.

Owls be found all over the world, in a variety o' habitats, from forests to deserts. They be an important part o' many ecosystems, helpin' to keep populations o' small mammals and birds under control.

So there ye have it, matey! Owls be amazin' creatures, with their big eyes, silent wings, and sharp talons. Now go forth and spread the word about these fascinatin' birds!
```

## Async

```python
prompt = ChatPromptTemplate.from_messages(
    [
        (
            "human",
            "what is the capital of {country}?",
        )
    ]
)

chain = prompt | llm
await chain.ainvoke({"country": "France"})
```

```output
AIMessage(content='The capital of France is Paris.', additional_kwargs={}, response_metadata={'finish_reason': 'stop', 'usage': {'acceptance_rate': 1, 'completion_tokens': 7, 'completion_tokens_after_first_per_sec': 442.126212227688, 'completion_tokens_after_first_per_sec_first_ten': 0, 'completion_tokens_per_sec': 46.28540439646366, 'end_time': 1731535343.0321083, 'is_last_response': True, 'prompt_tokens': 42, 'start_time': 1731535342.8808727, 'time_to_first_token': 0.137664794921875, 'total_latency': 0.15123558044433594, 'total_tokens': 49, 'total_tokens_per_sec': 323.99783077524563}, 'model_name': 'Meta-Llama-3.1-70B-Instruct', 'system_fingerprint': 'fastcoe', 'created': 1731535342}, id='c4b8c714-df38-4206-9aa8-fc8231f7275a')
```

## Async Streaming

```python
prompt = ChatPromptTemplate.from_messages(
    [
        (
            "human",
            "in less than {num_words} words explain me {topic} ",
        )
    ]
)
chain = prompt | llm

async for chunk in chain.astream({"num_words": 30, "topic": "quantum computers"}):
    print(chunk.content, end="", flush=True)
```

```output
Quantum computers use quantum bits (qubits) to process info, leveraging superposition and entanglement to perform calculations exponentially faster than classical computers for certain complex problems.
```

## Tool calling

```python
from datetime import datetime

from langchain_core.messages import HumanMessage, ToolMessage
from langchain_core.tools import tool


@tool
def get_time(kind: str = "both") -> str:
    """Returns current date, current time or both.
    Args:
        kind(str): date, time or both
    Returns:
        str: current date, current time or both
    """
    if kind == "date":
        date = datetime.now().strftime("%m/%d/%Y")
        return f"Current date: {date}"
    elif kind == "time":
        time = datetime.now().strftime("%H:%M:%S")
        return f"Current time: {time}"
    else:
        date = datetime.now().strftime("%m/%d/%Y")
        time = datetime.now().strftime("%H:%M:%S")
        return f"Current date: {date}, Current time: {time}"


tools = [get_time]


def invoke_tools(tool_calls, messages):
    available_functions = {tool.name: tool for tool in tools}
    for tool_call in tool_calls:
        selected_tool = available_functions[tool_call["name"]]
        tool_output = selected_tool.invoke(tool_call["args"])
        print(f"Tool output: {tool_output}")
        messages.append(ToolMessage(tool_output, tool_call_id=tool_call["id"]))
    return messages
```

```python
llm_with_tools = llm.bind_tools(tools=tools)
messages = [
    HumanMessage(
        content="I need to schedule a meeting for two weeks from today. "
        "Can you tell me the exact date of the meeting?"
    )
]
```

```python
response = llm_with_tools.invoke(messages)
while len(response.tool_calls) > 0:
    print(f"Intermediate model response: {response.tool_calls}")
    messages.append(response)
    messages = invoke_tools(response.tool_calls, messages)
    response = llm_with_tools.invoke(messages)

print(f"final response: {response.content}")
```

```output
Intermediate model response: [{'name': 'get_time', 'args': {'kind': 'date'}, 'id': 'call_7352ce7a18e24a7c9d', 'type': 'tool_call'}]
Tool output: Current date: 11/13/2024
final response: The meeting should be scheduled for two weeks from November 13th, 2024.
```

## Structured Outputs

```python
from pydantic import BaseModel, Field


class Joke(BaseModel):
    """Joke to tell user."""

    setup: str = Field(description="The setup of the joke")
    punchline: str = Field(description="The punchline to the joke")


structured_llm = llm.with_structured_output(Joke)

structured_llm.invoke("Tell me a joke about cats")
```

```output
Joke(setup='Why did the cat join a band?', punchline='Because it wanted to be the purr-cussionist!')
```

## Input Image

```python
multimodal_llm = ChatSambaNovaCloud(
    model="Llama-3.2-11B-Vision-Instruct",
    max_tokens=1024,
    temperature=0.7,
    top_p=0.01,
)
```

```python
import base64

import httpx

image_url = (
    "https://images.pexels.com/photos/147411/italy-mountains-dawn-daybreak-147411.jpeg"
)
image_data = base64.b64encode(httpx.get(image_url).content).decode("utf-8")

message = HumanMessage(
    content=[
        {"type": "text", "text": "describe the weather in this image in 1 sentence"},
        {
            "type": "image_url",
            "image_url": {"url": f"data:image/jpeg;base64,{image_data}"},
        },
    ],
)
response = multimodal_llm.invoke([message])
print(response.content)
```

```output
The weather in this image is a serene and peaceful atmosphere, with a blue sky and white clouds, suggesting a pleasant day with mild temperatures and gentle breezes.
```

## API reference

For detailed documentation of all SambaNovaCloud features and configurations head to the API reference: [docs.sambanova.ai/cloud/docs/get-started/overview](https://docs.sambanova.ai/cloud/docs/get-started/overview)
